Improved bacterial single-cell RNA-seq through automated MATQ-seq [scRNA-seq]
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA904727 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR22392900.fastq.gz | Fastqsanger.gz | |||
| SRR22393000.fastq.gz | Fastqsanger.gz | |||
| SRR22393100.fastq.gz | Fastqsanger.gz | |||
| SRR22393200.fastq.gz | Fastqsanger.gz | |||
| SRR22392901.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 384 |
Similar Datasets
Project description:Improved bacterial single-cell RNA-seq through automated MATQ-seq
| PRJNA904721 | ENA
Project description:In this work, we have improved our previously published bacterial single-cell RNA-sequencing protocol (MATQ-seq), providing enhancements that achieve a higher cell throughput while also including integration of automation. We selected a more efficient reverse transcriptase which led to a lower drop-out rate and higher workflow robustness, and we also successfully implemented a Cas9-based ribosomal RNA depletion protocol into the MATQ-seq workflow. Applying this improved protocol on a large set of single Salmonella cells sampled over growth revealed improved gene coverage and a higher gene detection limit, allowing us to reveal the expression of small regulatory RNAs such as GcvB or CsrB at a single-cell level. In addition, we were able to confirm previously described phenotypic heterogeneity in Salmonella in regards to expression of pathogenicity-associated genes.
2023-02-09 | GSE218632 | GEO
Project description:Improved bacterial single-cell RNA-seq through automated MATQ-seq [bulk RNA-seq]
| PRJNA904726 | ENA
Project description:In this work, we have improved our previously published bacterial single-cell RNA-sequencing protocol (MATQ-seq), providing enhancements that achieve a higher cell throughput while also including integration of automation. We selected a more efficient reverse transcriptase which led to a lower drop-out rate and higher workflow robustness, and we also successfully implemented a Cas9-based ribosomal RNA depletion protocol into the MATQ-seq workflow. Applying this improved protocol on a large set of single Salmonella cells sampled over growth revealed improved gene coverage and a higher gene detection limit, allowing us to reveal the expression of small regulatory RNAs such as GcvB or CsrB at a single-cell level. In addition, we were able to confirm previously described phenotypic heterogeneity in Salmonella in regards to expression of pathogenicity-associated genes.
2023-02-09 | GSE218631 | GEO
Project description:An Improved Bacterial Single-cell RNA-seq Reveals Biofilm Heterogeneity [scRNA-seq]
| PRJNA1081799 | ENA
Project description:In contrast to mammalian cells, bacterial cells lack mRNA polyadenylated tails, presenting a hurdle in isolating mRNA amidst the prevalent rRNA during single-cell RNA-seq. This study introduces a novel method, Ribosomal RNA-derived cDNA Depletion (RiboD), seamlessly integrated into the PETRI-seq technique, yielding RiboD-PETRI. This innovative approach offers a cost-effective, equipment-free, and high-throughput solution for bacterial single-cell RNA sequencing (scRNA-seq). By efficiently eliminating rRNA reads and substantially enhancing mRNA detection rates (up to 92%), our method enables precise exploration of bacterial population heterogeneity. Applying RiboD-PETRI to investigate biofilm heterogeneity, distinctive subpopulations marked by unique genes within biofilms were successfully identified. Notably, PdeI, a marker for the cell-surface attachment subpopulation, was observed to elevate cyclic diguanylate (c-di-GMP) levels, promoting persister cell formation. Thus, we address a persistent challenge in bacterial single-cell RNA-seq regarding rRNA abundance, exemplifying the utility of this method in exploring biofilm heterogeneity. Our method effectively tackles a long-standing issue in bacterial scRNA-seq: the overwhelming abundance of rRNA. This advancement significantly enhances our ability to investigate the intricate heterogeneity within biofilms at unprecedented resolution.
2024-12-10 | GSE260457 | GEO
Project description:Existing protocols for full-length single-cell RNA sequencing (scRNA-seq) produce libraries of high complexity (thousands of distinct genes) with outstanding sensitivity and specificity of transcript quantification. These full-length libraries have the advantage of allowing probing of transcript isoforms, are informative regarding single nucleotide polymorphisms, and allow assembly of the VDJ region of the T- and B-cell receptor sequences. Since full length protocols are mostly plate-based at present, they are also suited to profiling cell types where cell numbers are limiting, such as rare cell types during development for instance. A disadvantage of these methods has been the scalability and cost of the experiments, which has limited their popularity as compared to droplet-based and nanowell approaches. Here, we describe an automated protocol for full-length scRNA-seq, including both an in-house automated SMART-seq2 protocol, and a commercial kit-based workflow. We discuss these two protocols in terms of ease-of-use, equipment requirements, running time, cost per sample and sequencing quality. By benchmarking the lysis buffers, reverse transcription enzymes and their combinations, we propose an optimized in-house automated protocol with dramatically reduced cost. These pipelines have been employed successfully for several research projects allied with the Human Cell Atlas initiative (www.humancellatlas.org) and are available on protocols.io.
2020-12-31 | E-MTAB-9345 | biostudies-arrayexpress
Project description:With improved whole-cell isolation protocols, we performed single-cell RNA sequencing (scRNA-seq) and profiled the transcriptomes from adult non-human primate brain. We identified discriminative cell populations with canonical and novel markers. Cross-species projection demonstrated the evolutionary conservation among mouse, monkey, and human. This dataset serves as a detailed transcriptomic atlas for understanding the adult primate central nervous system.
2022-05-07 | E-MTAB-10225 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the SubSeries listed below.
2023-02-09 | GSE218633 | GEO
Project description:Currently, the detection of single nucleotide variants (SNVs) from 10 x Genomics single-cell RNA sequencing data (scRNA-seq) is typically performed on the pooled sequencing reads across all cells in a sample. Here, we assess the gaining of information regarding SNV assessments from individual cell scRNA-seq data, wherein the alignments are split by cellular barcode prior to the variant call. We also reanalyze publicly available data on the MCF7 cell line during anticancer treatment. We assessed SNV calls by three variant callers-GATK, Strelka2, and Mutect2, in combination with a method for the cell-level tabulation of the sequencing read counts bearing variant alleles-SCReadCounts (single-cell read counts). Our analysis shows that variant calls on individual cell alignments identify at least a two-fold higher number of SNVs as compared to the pooled scRNA-seq; these SNVs are enriched in novel variants and in stop-codon and missense substitutions. Our study indicates an immense potential of SNV calls from individual cell scRNA-seq data and emphasizes the need for cell-level variant detection approaches and tools, which can contribute to the understanding of the cellular heterogeneity and the relationships to phenotypes, and help elucidate somatic mutation evolution and functionality.
| S-EPMC8535975 | biostudies-literature